Using AI To Evaluate Data Sets In The Healthcare Space

Leveraging artificial intelligence is a transformative solution to long-standing issues in healthcare data management and patient outcomes, one that stands to ease the burden and create a balance for patients and healthcare providers alike.

Embracing this technology in the healthcare space is more important than ever. Utilizing AI to evaluate data sets does multiple things. It moves data out of silos, eliminates blind spots and puts invaluable pieces of information in communication with one another.

As a healthcare communications professional, I have first-hand experience using AI to make data-driven decisions, increase productivity and streamline marketing processes—all of which can make your life easier as well.

The Prognosis: Understanding What Ails You

The number of data sets available in the healthcare space can be overwhelming. From demographics and behavior to prescriptions and claims, all of these data sets sit in silos without a consistent method of consolidating the information and making it of value.

Siloed data is a direct result of today’s insurance-dependent healthcare system. In healthcare, data that sits in silos not only hinders patient care but also inhibits effective marketing, which is what I want to discuss.

Imagine you are a healthcare marketer, activating an annual campaign. After three years, a massive amount of data is available about the exposure of the campaign, including who saw the ad and what they did as a result.

If a physician saw the ad 20,000 times in a single year then wrote 1,000 scripts for the brand, this data is being collected but not connected. Using AI to evaluate data sets allows the healthcare marketer to utilize the collected data for planning purposes.

As for the problem? A dearth of tools—designed to collect and utilize available data wisely—means myriad benefits are getting missed.

The Remedy: AI Tools And Technology For Healthcare

Employing the right tools can do the heavy lifting associated with breaking down complex medical data for analysis. I like to think of the different AI functions as the left and right sides of the human brain.

Logical, left-brained tasks—such as data analysis—can be performed by identity resolution engines. These software tools, that use algorithms and machine learning to link data from multiple sources, create streamlined profiles for patients. This, in turn, allows for better understanding of individuals which improves both marketing and customer service.

Creative, right-brained tasks, such as marketing and campaign planning, fall to generative AI. By using machine learning models to understand new patterns from data, gen AI is able to literally generate new content (from images and text to music and videos) by making predictions based on newly acquired data patterns.

The challenge remains: Folks in the healthcare marketing space are largely unfamiliar with the tools available as many have not been tested correctly in the market. Still, options abound.

Personal content is something everyone seeks in healthcare. Gen AI can be leveraged to customize dynamic messaging for various types of patients. This saves time by eradicating the need for a human to create multiple different messages. Understanding what works and what doesn’t ultimately saves the dollar value that marketers are spending. In the testing environment, this helps with efficiency as well. The benefits of personalization extend to patient engagement and healthcare outcomes, making the use of AI compelling from a patient-care perspective as well.

The Results: Challenges Versus Benefits

All three stages of a marketing campaign can be connected to different parts of artificial intelligence.

During planning, AI helps in refining target audience insights. Analytical AI is key to running programs. By consolidating data from various sources—including patient demographics and healthcare provider input to prescriptions and claims—both campaign planning and patient education improves.

During execution,generative AI can be used to adjust messaging based on real-time feedback which leads to continuous optimization. In addition to assessing what type of language, triggers and prompts are working for your target audience, it provides a better understanding of the target audience.

Once a healthcare marketer can envision their target audience (using the demographic and psychographic data available to them), they are able to start building out their messaging which is when campaign activation can happen. What affinity do audience members have? What platforms do they prefer to learn from? Generative AI allows for specific options, like email or peer-to-peer learning, to be selected.

When data starts coming back to them—regarding everything from impression and location to script writing, for instance—this information is theoretically available to plan and execute the next campaign. In reality, when encountering huge data sets, it becomes a tedious task to go through the data and make a plan.

AI tools, on the other hand, can easily make sense of this data and optimize its utilization. If among the original target audience only 50% of the HCPs were high-value prescribers, a healthcare marketer would be able to deduce that 75% of their total budget should be allocated to this group—leaving a small, functional budget for the remaining target audience.

As the fourth quarter closes and the New Year approaches, the conversation surrounding AI in healthcare continues to evolve. If 2024 was all about making the move toward implementing it, then I predict 2025 will hinge on excelling in and absorbing AI for better campaign and budget planning. Why wait for the entire year to pass (and possibly fail) before implementing changes that could benefit your bottom line?

When compared with traditional methods of data analysis, using AI to evaluate healthcare data sets can speed up processes, reduce costs and improve decision-making precision. This year, beginning on January 1, I propose thinking about — and planning for — how dollar values will be allocated. By leveraging AI and utilizing smaller pockets of planning, if anything needs to change it can be done in real time.